As modern embedded systems are increasingly network connected, their protocol stacks expose themselves as a surface that is frequently attacked. While C-based implementations such as LwIP are efficient, their lack of memory safety induces critical vulnerabilities such as buffer overflows, dangling pointers, and use-after-free, leading to remote code execution or privilege escalation. In this paper, we present LwRustIP , a memory-safe embedded networking stack reimplemented in Rust and compatible with LwIP . We also share our development experience. LwRustIP replaces unsafe linked-list memory management with a custom allocator that honors the Rust ownership semantics, leverages zero-copy techniques for inter-layer packet handoffs, and applies lock-free object pools for concurrent buffer management. These design choices ensure memory safety while maintaining performance comparable to traditional C-based implementations. We deploy LwRustIP on ARM-based embedded platforms and evaluate its correctness, performance, and memory safety. Experimental results show that LwRustIP achieves memory safety without incurring measurable performance overhead compared to the original C-based implementation. Our experience highlights the practical challenges and benefits of using Rust for low-level system components and offers guidance for future efforts in memory-safe reengineering of legacy C codebases.

Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, merging the capabilities of sensing and communication to enhance efficiency and enable advanced applications. Mobile crowdsensing (MCS), as a important example of ISAC, leverages unmanned vehicles such as UAVs to continuously gather and transmit environmental data, supporting critical applications like traffic monitoring, urban congestion management, and accident investigation. In this paper, we focus on multi-task-oriented UAV crowdsensing (UCS), where diverse tasks—such as surveillance and emergency response—each have distinct age-of-information (AoI) requirements. We introduce a novel metric, the “valid task handling index,” to evaluate the performance of handling multiple tasks effectively. Our proposed hierarchical multi-agent deep reinforcement learning (MADRL) framework, DRL-MTUCS, integrates seamlessly with multi-agent actor-critic reinforcement learning methods. It features dynamically weighted queues for UAV goal assignment, enabling efficient management of multiple emergency tasks, and a low-level UAV execution module with a self-balancing intrinsic reward mechanism. This ensures all tasks are completed within their individual AoI constraints. Extensive experiments and trajectory visualizations validate the superior performance and robustness of DRL-MTUCS compared to six baselines across varying conditions, including the number of UAVs, surveillance task AoI thresholds, and emergency task image blur requirements.

Indoor localization is crucial for applications such as navigation, asset tracking, and emergency response. Fingerprint-based methods that use RSSI are widely adopted; however, they fail under large environmental changes. Unmanned Vehicles (UVs) equipped with high precision sensors are able to collect fingerprints, serving as a promising way by forming a Vehicular Crowdsensing (VCS) campaign. In this paper, we propose “BRAVE”, a Bayesian RL Approach for VCS under Environment changing, while introducing a new metric “Calibration Benefit” to explicitly quantify how effectively a learned trajectory updates those regions of the fingerprint database that have changed and matter most for localization. Specifically, we propose a spatial-temporal Bayesian Network(BN) for change detection, a region rearrangement method for fewer restarts, and an optimistic strategy to balance the exploration and exploitation trade-offs in optimizing calibration benefit. Extensive results on two real-world datasets from SML Center (Shanghai) and Haopu Fashion City (Shanghai) demonstrate that BRAVE outperforms eight baselines and the derived dataset has better localization accuracy compared with the original dataset.

Grounding natural language instructions to visual observations is fundamental for embodied agents operating in open-world environments. Recent advances in visual-language mapping have enabled generalizable semantic representations by leveraging visionlanguage models (VLMs). However, these methods often fall short in aligning free-form language commands with specific scene instances, due to limitations in both instance-level semantic consistency and instruction interpretation. We present OpenMap, a zero-shot open-vocabulary visual-language map designed for accurate instruction grounding in navigation tasks. To address semantic inconsistencies across views, we introduce a Structural-Semantic Consensus constraint that jointly considers global geometric structure and vision-language similarity to guide robust 3D instancelevel aggregation. To improve instruction interpretation, we propose an LLM-assisted Instruction-to-Instance Grounding module that enables fine-grained instance selection by incorporating spatial context and expressive target descriptions. We evaluate OpenMap on ScanNet200 and Matterport3D, covering both semantic mapping and instruction-to-target retrieval tasks. Experimental results show that OpenMap outperforms state-of-the-art baselines in zero-shot settings, demonstrating the effectiveness of our method in bridging free-form language and 3D perception for embodied navigation.

By harnessing the capabilities of unmanned aerial and ground vehicles (UAVs and UGVs), equipped with high-precision sensors, air-ground mobile crowdsensing (AG-MCS) has proven to be effective for data collection in urban environments. In this paper, by optimizing the metric of age-of-information (AoI) that measures the freshness of collected data, we consider the problem of AoI-Aware AG-MCS (A3G-MCS), where UGVs dispatch UAVs from multiple UGV stops to collect data from point-of-interests (PoIs). We propose a novel multi-agent curriculum learning framework called “MACL(MCS)”, that explicitly balances the individual and team goals of both UAV/UGV controllers to facilitate the exploration of policy towards globally-optimal performance. It is further enhanced by a UAV/UGV collaborative observation augmentation (COA) module for improved inter-controller communication. Extensive results reveal that MACL(MCS) consistently outperforms five baselines, and achieves comparable performance to exact method with better scalability and efficiency. It also showcases strong generalization capability towards real-world scenarios on both TSPLIB and Purdue, KAIST and NCSU datasets.

Permissioned blockchains are gaining traction for enterprise applications due to their enhanced security and performance characteristics. However, they face significant scalability challenges, particularly in environments with high concurrency and diverse workload demands. In this paper, we present a novel architecture for scaling permissioned blockchains by disaggregating Log-Structured Merge (LSM) trees between execution and storage nodes. Our approach leverages the inherent structure of LSM-trees to efficiently handle write-intensive workloads by separating recent updates in execution nodes and long-term data storage in storage nodes. This design supports elastic scaling of both compute and memory resources, enabling independent and efficient resource utilization. We introduce the concept of Semi-stateful Nodes, which balance the benefits of fully-stateful and stateless nodes. This approach reduces communication overhead during parallel transaction execution while simultaneously improving scalability. Our architecture also includes a parallel transaction execution algorithm to minimize Cross-Shard Transactions (CSTs) and enhance overall system performance. Through our experimentation, we demonstrate that our system achieves significant performance improvements, including up to $15 \times$ increase in throughput compared to traditional architectures. The results indicate that our approach significantly reduces the overhead and time consumption during scalability operations in permissioned blockchains, making it a system that can achieve high scalability and good performance at a low cost.

Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. This requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallel processing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. The idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless Scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations.

In recent years, large language models (LLMs) have shown rapid development, becoming one of the most popular topics in the field of artificial intelligence. LLMs have demonstrated powerful generalization and learning capabilities, and their performance on various language tasks has been remarkable. Despite their successes, LLMs face significant challenges, particularly in domain-specific tasks that require structured knowledge, often leading to issues such as hallucinations. To mitigate these challenges, we propose a novel system, SynaptiQA, which integrates LLMs with Knowledge Graphs (KGs) to answer more questions about knowledge. Our approach leverages the generative capabilities of LLMs to create and optimize KG queries, thereby improving the accuracy and contextual relevance of responses. Experimental results in an industrial data set demonstrate that SynaptiQA outperforms baseline models and naive retrieval-augmented generation (RAG) systems, demonstrating improved accuracy and reduced hallucinations. This integration of KGs with LLMs paves the way for more reliable and interpretable domain-specific question answering systems.

The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource-constrained distributed frameworks. To address this issue, we propose Triple-partition Network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition Network on a state-of-art platform and show that trained Triple-partition Network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.